Your new experience awaits. Try the new design now and help us make it even better

CORRECTION article

Front. Oncol., 26 January 2026

Sec. Gynecological Oncology

Volume 16 - 2026 | https://doi.org/10.3389/fonc.2026.1790105

Correction: Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer

This article is a correction to:

  1. Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer

    1. Read original article
Chenwen SunChenwen Sun1Qianling LiQianling Li2Yanan HuangYanan Huang1Yang XiaYang Xia3Meiping LiMeiping Li3Xiucong ZhuXiucong Zhu1Jinke ZhuJinke Zhu1Zhenhua Zhao*Zhenhua Zhao1*
  • 1Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
  • 2School of Medicine, Graduate School, Zhejiang University, Hangzhou, China
  • 3Department of Radiology, Shaoxing Maternity and Child Health Care Center, Shaoxing, China

A Correction on
Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer

By Sun C, Li Q, Huang Y, Xia Y, Li M, Zhu X, Zhu J and Zhao Z (2026) Front. Oncol. 15:1502370. doi: 10.3389/fonc.2025.1502370

Affiliation 1Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China was erroneously given as 2School of Medicine, Graduate School, Zhejiang University, Hangzhou, China for author Qianling Li.

Affiliation 2School of Medicine, Graduate School, Zhejiang University, Hangzhou, China.

was erroneously given as 1Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China for author Yanan Huang, Xiucong Zhu, Jinke Zhu and Zhenhua Zhao.

The original version of this article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: endometrial carcinoma, machine learning, Pten, PIK3, mTOR, targeted therapy

Citation: Sun C, Li Q, Huang Y, Xia Y, Li M, Zhu X, Zhu J and Zhao Z (2026) Correction: Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer. Front. Oncol. 16:1790105. doi: 10.3389/fonc.2026.1790105

Received: 17 January 2026; Accepted: 20 January 2026; Revised: 19 January 2026;
Published: 26 January 2026.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2026 Sun, Li, Huang, Xia, Li, Zhu, Zhu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhenhua Zhao, emhhbzIwNzVAMTYzLmNvbQ==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.